Enhanced medical image segmentation using novel level set evolution and efficient optimization

Accurate and efficient medical image segmentation is a critical yet challenging task due to issues like intensity inhomogeneity, poor contrast, noise, and blur. In this paper, we introduce a novel framework that addresses these challenges by leveraging adaptive level set evolution, enhanced with a unique edge indication function. Unlike prior edge-based algorithms, which frequently fail with noisy images and have large computing costs, our method incorporates an improved edge indicator term into the level set architecture, considerably improving performance on degraded images. The efficiency of proposed model depends on the optimization and implementation of proximal alternating direction technique of multipliers (). Our findings were validated using qualitative and quantitative methods such as dice coefficient assessment, sensitivity, accuracy, and mean absolute distance (MAD). Experimental findings show that the model successfully detects boundaries of objects within noisy and blurred visual data. The algorithm showed exceptional precision through its average dice coefficient of 0.96 which matched the ground truth data measurement standards. The system runs efficiently for only 0.90 seconds on average as a performance result. The framework achieved standout performance metrics that included 0.9552 accuracy together with 0.8854 sensitivity and 0.0796 MAD. The framework demonstrates robust capabilities in medical image evaluation which makes it an optimistic instrument for advancing the field.